Phillip Honenberger (University of Nevada, Las Vegas), Evelyn Brister (Rochester Institute of Technology)
Bibliometrics is now an established method for investigating scientific knowledge production and its social dynamics. This project analyzes contributor sections in order to better understand the collaborative dynamics of scientific research teams.
Since 2009, scientific journals such as Nature and PNAS have adopted a protocol according to which co-authored research papers explicitly specify each author’s contribution – that is, they identify which authors performed experiments, analyzed data, contributed materials, or wrote the paper. Though not unbiased, these contributor sections provide explicit representations of the division of credit, responsibility, and labor type in research teams. Here we present methods for analyzing this data source, and we use the results to suggest and test theses about scientific collaboration and interdisciplinary integration.
In a pilot study, we used webscraping techniques to collect and analyze contributor sections from 333 articles that had been published in PNAS in March-May 2017. We found distinct differences in the reporting of certain forms of labor between disciplines and a greater variety of types of labor reported in some disciplines as compared to others. The current study expands the dataset and number of disciplines to include all articles published in PNAS from 2013 to the present (~10,000 articles).
First, we analyze patterns of distribution of scientific labor and connect these to philosophical questions concerning differences in collaborative practice between disciplines. We identify reasons why some differences might be due to reporting bias and others to differences in collaborative norms. We also identify how collaborative dynamics are related to size of research team. This sort of study bridges the gap between descriptive social scientific studies of team science and mathematical/conceptual models of the division of cognitive labor (e.g., Muldoon 2018, Bruner and O’Connor 2018).
Second, we analyze this dataset to evaluate the forms of labor contributed by authors to multidisciplinary collaborations. We identify authors’ disciplinary affiliation, enabling an analysis of cross-disciplinary contributions based on the journal’s disciplinary classification of articles. For instance, we identify the type of labor most often contributed by statisticians to papers not classified as mathematics. Additionally, we evaluate a measure of the evenness of the division of cognitive labor proposed by Larivière et al. (2016) and propose a method for using the evenness of labor distribution to test hypotheses about the processes that foster interdisciplinary integration (Wagner 2011).
The project illustrates both the promises and challenges of using empirical approaches in philosophical research.
